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The EZ diffusion model provides a powerful test of simple empirical effects

Overview of attention for article published in Psychonomic Bulletin & Review, June 2016
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Title
The EZ diffusion model provides a powerful test of simple empirical effects
Published in
Psychonomic Bulletin & Review, June 2016
DOI 10.3758/s13423-016-1081-y
Pubmed ID
Authors

Don van Ravenzwaaij, Chris Donkin, Joachim Vandekerckhove

Abstract

Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59-108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3-22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data-generating full diffusion model.

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The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
United States 1 <1%
Switzerland 1 <1%
Unknown 126 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 23%
Researcher 23 18%
Student > Master 17 13%
Student > Bachelor 13 10%
Student > Postgraduate 10 8%
Other 16 12%
Unknown 20 16%
Readers by discipline Count As %
Psychology 65 50%
Neuroscience 17 13%
Business, Management and Accounting 4 3%
Engineering 4 3%
Social Sciences 3 2%
Other 9 7%
Unknown 27 21%